Skip to main content

Big Data: maximising the oil and gas supply chain

Hydrocarbon Engineering,


Everyone is talking about Big Data these days: how to capture it; how to manage it; how to store it; how to use it. Businesses that deal in physical commodities, such as oil and gas, are no exception. But what is interesting about managing commodities is that it has always been a big data business – long before the term became fashionable.

By its very nature, commodity trading creates thousands of individual data points. Physical trades that are hedged with financial derivatives. International supply chains with touch points in every continent. Pan-global networks for storage and transport. Overlapping layers of national and supra-national legislation. And a crowd of creditors, customers, counterparties and contractors circling at every stage.

Behind all these data points are decisions to be made that affect short-term profitability and long-term stability. If we look at the oil and gas supply chain, simply moving oil from point A to point B involves selecting the right storage, the right transport solution and the right route. That’s after deciding on the right quantity to move, the right market to send it to – and of course the right source to acquire it from in the first place. In rapidly moving energy markets, any of those decisions may need to be reconsidered before the commodity arrives at its final destination.

Then there’s the processing and refining to consider. Is it better to move goods to a refinery or factory, or is it preferable to sell and move the unrefined commodity? How much needs to be made, and of what quality? Are there more buyers for light crude versus heavy crude oil? What grade would drive the greatest profit margin? Are there storage, pipeline and transport facilities that can ensure the optimum quality and integrity? How can asset-intensive stockyards with multimillion-dollar machinery be optimised to get the most throughput and the most effective use of equipment?

The risk management aspect of the business adds a whole new layer of data points too. Prices move, markets change, regulations get updated. Oil and gas companies with exposure to commodities need to stay abreast of these developments and react accordingly. They also have to decide on risk policies that take into account the counterparties, geographies and commodities they wish to be exposed to, and the limits they wish to put in place on those exposures. Again, the ability to react as these limits are approached or breached – deliberately or otherwise – is essential.

The oil and gas industry as a big data business

Taken together these individual dimensions create a significant amount of complexity – and produce huge volumes of data. But if the oil and gas industry has always been a big data business, it hasn’t always been the best at using that data.

Most commodity exposed firms have reasonably strong transactions systems along the supply chain to capture the data that surrounds each transaction or engagement. But as margins tighten, and conditions get tougher, it is becoming increasingly clear that systems need to do more: they need to be able to correctly interpret the data so that it can support real-time decision-making.

Moving from data capture to data analytics

This is a big step on the evolution of commodity management systems: from data capture to data analytics. The ability to analyse information to create predictive models allows firms to develop accurate, repeatable formulae that take into account market conditions to identify optimal scenarios.

Going back to the beginning of the supply chain gives us an idea of what this might look like in practice. As we have established, matching both the quality and quantity of raw materials, with the right transport and logistics to meet the specific demand of an identified buyer reduces inefficiency in the supply chain.

The problem here is that the initial production of the commodity is based on the assumption that the right amount and the right quality will be available. Looking at an example from the coal industry: planning the entire supply chain depends on the coalmine producing the right amount of coal at the right quality. If the mined coal contains insufficient iron, then the carefully calibrated supply chain starts to crumble. Hope rather than fact has been the foundation of all subsequent transactions.

A system designed for the Big Data era can remove much of that uncertainty. By incorporating and analysing historic yield information or geological information for example, the system can create a far more accurate picture of the likely outcome of any given well or mine. And with the ability to predict both quality and quantity of output, the commodities business is in a better position to decide which producers to deal with, find an appropriate buyer, enter into advanced agreements and negotiate better pricing, as well as optimised logistics planning.

Advanced visualisation techniques

In this way, commodities businesses can make better use of the information they produce themselves as well as externally available data sets. Using advanced visualisation techniques coupled with user-controlled, predictive analytics, oil and gas companies can optimise trading and supply chain operations.

The right system will be able to integrate all of these internal and external data sets, extracted from multiple sources, and bring them together in a central repository. It will then visualise that data to enable firms to pinpoint where they and their resources need to be at any time.

By using powerful analytical capabilities, the new generation of commodity management systems, such as Eka’s Smart Commodity Management solution, enable commodity exposed businesses to develop predictive capabilities that facilitate actual decision-making, rather than just monitoring activity. In other words, it’s about looking forward, rather than back: making accurate forecasts about the future, rather than just monitoring what’s happened in past.

The world has changed. KPIs and static graphs are great – but they are not enough. The better solution allows businesses that deal in oil and gas to embrace both Big Data and the future without being overwhelmed by either.

Written by Michael Schwartz, Executive Vice President & Chief Marketing Officer, and Shobhit Mathur, Vice President, Product Management & Pre Sales, Eka.


Edited by Cecilia Rehn.

Read the article online at: https://www.hydrocarbonengineering.com/gas-processing/22042014/big_data_maximising_the_oil_and_gas_supply_chain/


 

Embed article link: (copy the HTML code below):